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An improved face recognition technique based on modular PCA approach

机译:一种基于模块化PCA方法的改进人脸识别技术

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A face recognition algorithm based on modular PCA approach is presented in this paper. The proposed algorithm when compared with conventional PCA algorithm has an improved recognition rate for face images with large variations in lighting direction and facial expression. In the proposed technique, the face images are divided into smaller sub-images and the PCA approach is applied to each of these sub-images. Since some of the local facial features of an individual do not vary even when the pose, lighting direction and facial expression vary, we expect the proposed method to be able to cope with these variations. The accuracy of the conventional PCA method and modular PCA method are evaluated under the conditions of varying expression, illumination and pose using standard face databases.
机译:提出了一种基于模块化PCA方法的人脸识别算法。与常规PCA算法相比,该算法对人脸图像的识别率有所提高,且人脸图像的光照方向和面部表情变化很大。在提出的技术中,面部图像被分成较小的子图像,并且将PCA方法应用于这些子图像中的每一个。由于即使姿势,照明方向和面部表情发生变化,一个人的某些局部面部特征也不会发生变化,因此我们希望所提出的方法能够应对这些变化。使用标准的人脸数据库,在变化的表情,光照和姿势的条件下,评估常规PCA方法和模块化PCA方法的准确性。

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